Systems and methods for inspecting absorbent articles on a converting line

ABSTRACT

A method for inspecting absorbent articles is provided. The inspection is performed using an inspection algorithm generated with a convolutional neural network having convolutional neural network parameters. The convolutional neural network parameters are generated by a training algorithm. Based on the inspection, characteristics of the absorbent articles, such as defects, can be identified. Absorbent articles having identified characteristics can be rejected, or other actions can be taken.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/993,698, filed on May 31, 2018, which claims the benefit of U.S.Provisional Application No. 62/518,711, filed Jun. 13, 2017, both ofwhich are herein incorporated by reference.

FIELD OF THE INVENTION

The present disclosure relates to systems and methods for manufacturingdisposable absorbent articles, and more particularly, systems andmethods for inspecting substrates and components on a convertingapparatus utilizing an inspection algorithm generated with aconvolutional neural network.

BACKGROUND OF THE INVENTION

Along an assembly line, diapers and various types of other absorbentarticles may be assembled by adding components to and otherwisemodifying an advancing, continuous web of material. For example, in someprocesses, advancing webs of material are combined with other advancingwebs of material. In other examples, individual components created fromadvancing webs of material are combined with advancing webs of material,which in turn, are then combined with other advancing webs of material.Webs of material and component parts used to manufacture diapers mayinclude: backsheets, topsheets, absorbent cores, front and/or back ears,fastener components, and various types of elastic webs and componentssuch as leg elastics, barrier leg cuff elastics, and waist elastics.Once the desired component parts are assembled, the advancing web(s) andcomponent parts are subjected to a final knife cut to separate theweb(s) into discrete diapers or other absorbent articles. The discretediapers or absorbent articles may also then be folded and packaged.

For quality control purposes, absorbent article converting lines mayutilize various types of sensor technology to inspect the webs anddiscrete components added to the webs along the converting line asabsorbent articles are constructed. Example sensor technology mayinclude vision systems, photoelectric sensors, proximity sensors, laseror sonic distance detectors, and the like. Product inspection data fromthe sensors may be communicated to a controller in various ways. Inturn, the controller may be programmed to receive product inspectiondata, and in turn, make adjustments to the manufacturing process. Insome instances, the controller may reject defective absorbent articlesbased on the product inspection data after the final knife cut at theend of the converting line.

As such, the controller may be programmed with various algorithmsadapted to analyze the inspection data and provide desired controlfunctions to the manufacturing process. The complexity andsophistication of the algorithms may vary with the type of inspectiondata being analyzed. In some configurations, a vision system may beconfigured to communicate image data from an inspection zone to acontroller, wherein aspects of the image data may be analyzed by analgorithm to determine whether control actions should be executed. Forexample, the image data may be analyzed to determine whether a componentis missing or improperly positioned during the assembly process.Depending upon whether other features, such as graphics, bond patterns,or wrinkles, also may be located in the inspection zone, the algorithmmay need to be relatively more complex to enable the algorithm todistinguish the component or lack thereof from such other features inthe inspection zone.

However, limitations of human generated inspection algorithms currentlyconstrain the speed of algorithm development and the scope of what canbe inspected. Consequently, it would be beneficial to generate andutilize algorithms capable of inspections that otherwise cannot be codedby humans to conduct relatively more sophisticated in-process inspectionoperations of assembled absorbent articles.

SUMMARY OF THE INVENTION

In one form, a method comprises preprocessing each image of a group ofimages of absorbent articles to label a component of the absorbentarticle. The method further comprises generating a first inspectionalgorithm with a convolutional neural network based on the preprocessedimages. The first inspection algorithm is usable to mask the labeledcomponent in images of absorbent articles. The method further comprisesproviding a second inspection algorithm. The second inspection algorithmis usable to determine one or more properties of the labeled componentin images of absorbent articles. The one or more properties comprise anyof a location, a size, a shape, and a presence of the component. Themethod further comprises providing a communication network, connecting asensor with the communication network and connecting a controller withthe communication network. The controller comprises the first inspectionalgorithm. The method further comprises advancing a substrate through aconverting process, sequentially adding component parts to thesubstrate, and creating images of at least one of the substrate andcomponent parts with the sensor. The method further comprisescommunicating the images from the sensor to the controller and maskingthe labeled component in the images by analyzing the images with thefirst inspection algorithm. The method further comprises determining atleast one of the properties for the labeled component in the substratewith the second inspection algorithm. The method further comprisescutting the substrate with component parts added thereto into discreteabsorbent articles. The method further comprises based on thedetermination of the least one of the properties for the labeledcomponent in the substrate, executing a control action. The controlaction is any of rejecting one or more of the discrete absorbentarticles, an automatic phase adjustment, an automatic trackingadjustment, a machine maintenance scheduling, and a machine stopcommand.

In another form, a method comprises preprocessing each image of a groupof images of absorbent articles to label a component of the absorbentarticle. The method further comprises generating a first inspectionalgorithm with a convolutional neural network based on the preprocessedimages. The first inspection algorithm is usable to mask the labeledcomponent in images of absorbent articles. The method further comprisesproviding a second inspection algorithm. The second inspection algorithmis usable to determine one or more properties of the labeled componentin images of absorbent articles. The one or more properties comprise anyof a location, a size, a shape, and a presence of the component. Themethod further comprises advancing a substrate through a convertingprocess and sequentially adding component parts to the substrate. Themethod further comprises creating images of a substrate and one or morecomponent parts. The method further comprises masking the labeledcomponent in the images by analyzing the images with the firstinspection algorithm. The method further comprises determining at leastone of the properties for the labeled component in the substrate withthe second inspection algorithm.

In yet another form, a method comprises storing a group of digitizedsignals of absorbent articles. The digitized signals are preprocessed tolabel a component of the absorbent article. The method further comprisesproviding the preprocessed digitized signals to a training algorithm tocreate a convolutional neural network and generating a first inspectionalgorithm with the convolutional neural network based on the group ofpreprocessed digitized signals. The first inspection algorithm masks oneor more properties of the labeled component. The method furthercomprises providing a second inspection algorithm. The second inspectionalgorithm is usable to determine one or more properties of the labeledcomponent in digitized signals of absorbent articles. The one or moreproperties comprise any of a location, a size, a shape, and a presenceof the component. The method further comprises advancing a substratethrough a converting process, and sequentially adding component parts tothe substrate. The method further comprises creating inspection data ofat least one of the substrate and component parts with a sensor andcommunicating the inspection data from the sensor to a controller. Thecontroller comprises the first inspection algorithm. The method furthercomprises identifying the labeled component in the substrate byanalyzing the inspection data with the first inspection algorithm. Themethod further comprises determining at least one of the properties forthe labeled component in the substrate with the second inspectionalgorithm. The method further comprises cutting the substrate withcomponent parts added thereto into discrete absorbent articles.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a partially cut away plan view of an absorbent article in theform of a taped diaper that may include one or more substrates and/orcomponents inspected and/or controlled in accordance with the presentdisclosure with the portion of the diaper that faces away from a weareroriented towards the viewer.

FIG. 1B is a plan view of the absorbent article of FIG. 1A that mayinclude one or more substrates and/or components monitored and/orcontrolled in accordance with the present disclosure with the portion ofthe diaper that faces toward a wearer oriented towards the viewer.

FIG. 2A is a front perspective view of an absorbent article in the formof a diaper pant that may include one or more substrates and/orcomponents inspected and/or controlled in accordance with the presentdisclosure.

FIG. 2B is a front view of the absorbent article of FIG. 2A.

FIG. 2C is a rear view of the absorbent article of FIG. 2A.

FIG. 3 is a schematic representation of an absorbent article convertingline and control system.

FIG. 4 is a top view of an advancing substrate showing virtual productsand virtual segments.

FIG. 5 is a block diagram of a convolutional neural network learningparadigm for determining convolutional neural network parameters for aninspection algorithm of a convolutional neural network for convertingline inspection.

FIG. 6 is a block diagram of a convolutional neural network learningparadigm for determining convolutional neural network parameters for aninspection algorithm utilizing a feedback control loop from a convertingline.

FIG. 7 is a block diagram of a convolutional neural network learningparadigm for determining convolutional neural network parametersutilizing saliency filtering to pre-process training images.

FIG. 8 is a block diagram of a centralized training computing systempositioned remote from a plurality of production environments.

FIG. 9 shows an example set of images from a converting line in whichthe landing zone has been identified in accordance with the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

The following term explanations may be useful in understanding thepresent disclosure: “Absorbent article” is used herein to refer toconsumer products whose primary function is to absorb and retain soilsand wastes. Absorbent articles can comprise sanitary napkins, tampons,panty liners, interlabial devices, wound dressings, wipes, disposablediapers including taped diapers and diaper pants, inserts for diaperswith a reusable outer cover, adult incontinent diapers, adultincontinent pads, and adult incontinent pants. The term “disposable” isused herein to describe absorbent articles which generally are notintended to be laundered or otherwise restored or reused as an absorbentarticle (e.g., they are intended to be discarded after a single use andmay also be configured to be recycled, composted or otherwise disposedof in an environmentally compatible manner).

The term “taped diaper” (also referred to as “open diaper”) refers todisposable absorbent articles having an initial front waist region andan initial back waist region that are not fastened, pre-fastened, orconnected to each other as packaged, prior to being applied to thewearer. A taped diaper may be folded about the lateral centerline withthe interior of one waist region in surface to surface contact with theinterior of the opposing waist region without fastening or joining thewaist regions together. Example taped diapers are disclosed in varioussuitable configurations U.S. Pat. Nos. 5,167,897, 5,360,420, 5,599,335,5,643,588, 5,674,216, 5,702,551, 5,968,025, 6,107,537, 6,118,041,6,153,209, 6,410,129, 6,426,444, 6,586,652, 6,627,787, 6,617,016,6,825,393, and 6,861,571; and U.S. Patent Publication Nos. 2013/0072887A1; 2013/0211356 A1; and 2013/0306226 A1.

The term “pant” (also referred to as “training pant”, “pre-closeddiaper”, “diaper pant”, “pant diaper”, and “pull-on diaper”) refersherein to disposable absorbent articles having a continuous perimeterwaist opening and continuous perimeter leg openings designed for infantor adult wearers. A pant can be configured with a continuous or closedwaist opening and at least one continuous, closed, leg opening prior tothe article being applied to the wearer. A pant can be preformed orpre-fastened by various techniques including, but not limited to,joining together portions of the article using any refastenable and/orpermanent closure member (e.g., seams, heat bonds, pressure welds,adhesives, cohesive bonds, mechanical fasteners, etc.). A pant can bepreformed anywhere along the circumference of the article in the waistregion (e.g., side fastened or seamed, front waist fastened or seamed,rear waist fastened or seamed). Example diaper pants in variousconfigurations are disclosed in U.S. Pat. Nos. 5,246,433; 5,569,234;6,120,487; 6,120,489; 4,940,464; 5,092,861; 5,897,545; 5,957,908; andU.S. Patent Publication No. 2003/0233082 A1.

An “elastic,” “elastomer” or “elastomeric” refers to materialsexhibiting elastic properties, which include any material that uponapplication of a force to its relaxed, initial length can stretch orelongate to an elongated length more than 10% greater than its initiallength and will substantially recover back to about its initial lengthupon release of the applied force.

As used herein, the term “joined” encompasses configurations whereby anelement is directly secured to another element by affixing the elementdirectly to the other element, and configurations whereby an element isindirectly secured to another element by affixing the element tointermediate member(s) which in turn are affixed to the other element.

The term “substrate” is used herein to describe a material which isprimarily two-dimensional (i.e. in an XY plane) and whose thickness (ina Z direction) is relatively small (i.e. 1/10 or less) in comparison toits length (in an X direction) and width (in a Y direction).Non-limiting examples of substrates include a web, layer or layers orfibrous materials, nonwovens, films and foils such as polymeric films ormetallic foils. These materials may be used alone or may comprise two ormore layers laminated together. As such, a web is a substrate.

The term “machine direction” (MD) is used herein to refer to thedirection of material flow through a process. In addition, relativeplacement and movement of material can be described as flowing in themachine direction through a process from upstream in the process todownstream in the process.

The term “cross direction” (CD) is used herein to refer to a directionthat is generally perpendicular to the machine direction.

The present disclosure relates to systems and processes manufacturingabsorbent articles. More particularly, the systems and processes hereinmay be configured to inspect substrates and components on a convertingapparatus utilizing an inspection algorithm generated with aconvolutional neural network. During the manufacture of absorbentarticles, a substrate may be advanced through a converting process whilecombining with other substrates and/or adding component parts to thesubstrate. In turn, the substrate with component parts added thereto maybe cut into discrete absorbent articles. As such, an inspection systemmay be configured to inspect the substrate and/or component parts duringthe assembly process. As discussed in more detail below, the inspectionsystem may include a controller and a sensor. The controller includes aninspection algorithm generated with a convolutional neural network basedon convolutional neural network parameters. The inspection algorithm maybe created based in part from a group of images of absorbent articles.Each of the images may be preprocessed to label a component of theabsorbent article. The component may be, for instance, a landing zone,an ear, a tab, among a variety of other features or attributes,including contaminants such as a foreign material, grease, or dirt, orother undesirable transformation of the absorbent article such a tear, awrinkle, a fold, and so forth. In addition to labeling a component,preprocessing the image can optionally include cropping the image,scaling the image, resizing the image, and so forth. The preprocessedimages may then be provided to a training algorithm to create theconvolutional neural network parameters used to generate the inspectionalgorithm. During inspection operations, the sensor may be adapted tocreate inspection data of at least one of the substrate and componentparts, and then communicate the inspection data to the controller. Thelabeled components of the substrate and component parts from thetraining set may then be identified by analyzing the inspection datawith the inspection algorithm. Subsequent to identifying the labeledcomponent, a second inspection algorithm may then be utilized toidentify one or more properties of the labeled component. In turn, thecontroller may execute a control action based on properties of theidentified component.

It is to be appreciated that the systems and methods disclosed hereinare applicable to work with various types of converting processes and/ormachines, such as for example, absorbent article manufacturing, and/orpackaging processes. For illustration purposes, the methods andapparatuses are discussed below in the context of manufacturing diapers,although this disclosure is not so limited. The methods and apparatusesare applicable to converting processes for feminine hygiene products,for example. For the purposes of a specific illustration, FIGS. 1A and1B show an example of an absorbent article 100 that may be assembled inaccordance with the methods and apparatuses disclosed herein. Inparticular, FIG. 1A shows one example of a plan view of an absorbentarticle 100 configured as a taped diaper 100T, with the portion of thediaper that faces away from a wearer oriented towards the viewer. AndFIG. 1B shows a plan view of the diaper 100 with the portion of thediaper that faces toward a wearer oriented towards the viewer. The tapeddiaper 100T shown in FIGS. 1A and 1B includes a chassis 102, first andsecond rear side panels 104 and 106; and first and second front sidepanels 108 and 110.

As shown in FIGS. 1A and 1B, the diaper 100 and the chassis 102 eachinclude a first waist region 116, a second waist region 118, and acrotch region 119 disposed intermediate the first and second waistregions. The first waist region 116 may be configured as a front waistregion, and the second waist region 118 may be configured as back waistregion. In some configurations, the length of each of the front waistregion, back waist region, and crotch region may be ⅓ of the length ofthe absorbent article 100. The absorbent article may also include alaterally extending front waist edge 120 in the front waist region 116and a longitudinally opposing and laterally extending back waist edge122 in the back waist region 118. To provide a frame of reference forthe present discussion, the diaper 100T in FIGS. 1A and 1B is shown witha longitudinal axis 124 and a lateral axis 126. The longitudinal axis124 may extend through a midpoint of the front waist edge 120 andthrough a midpoint of the back waist edge 122. And the lateral axis 126may extend through a midpoint of a first longitudinal or right side edge128 and through a midpoint of a second longitudinal or left side edge130.

As shown in FIGS. 1A and 1B, the diaper 100 includes an inner, bodyfacing surface 132, and an outer, garment facing surface 134. And thechassis 102 may include a backsheet 136 and a topsheet 138. The chassis102 may also include an absorbent assembly 140, including an absorbentcore 142, disposed between a portion of the topsheet 138 and thebacksheet 136. As discussed in more detail below, the diaper 100 mayalso include other features, such as leg elastics and/or leg cuffs, anelastic waist region, and/or flaps, e.g., side panels and/or ears, toenhance the fits around the legs and waist of the wearer, to enhance thefit around the legs of the wearer.

As shown in FIGS. 1A and 1B, the periphery of the chassis 102 may bedefined by the first longitudinal side edge 128, a second longitudinalside edge 130, a first laterally extending end edge 144 disposed in thefirst waist region 116, and a second laterally extending end edge 146disposed in the second waist region 118. Both side edges 128 and 130extend longitudinally between the first end edge 144 and the second endedge 146. As shown in FIG. 1A, the laterally extending end edges 144 and146 may form a portion of the laterally extending front waist edge 120in the front waist region 116 and a portion of the longitudinallyopposing and laterally extending back waist edge 122 in the back waistregion 118. The distance between the first lateral end edge 144 and thesecond lateral end edge 146 may define a pitch length, PL, of thechassis 102. When the diaper 100 is worn on the lower torso of a wearer,the front waist edge 120 and the back waist edge 122 may encircle aportion of the waist of the wearer. At the same time, the side edges 128and 130 may encircle at least a portion of the legs of the wearer. Andthe crotch region 119 may be generally positioned between the legs ofthe wearer with the absorbent core 142 extending from the front waistregion 116 through the crotch region 119 to the back waist region 118.

It is to also be appreciated that a portion or the whole of the diaper100 may also be made laterally extensible. The additional extensibilitymay help allow the diaper 100 to conform to the body of a wearer duringmovement by the wearer. The additional extensibility may also help, forexample, the user of the diaper 100, including a chassis 102 having aparticular size before extension, to extend the front waist region 116,the back waist region 118, or both waist regions of the diaper 100and/or chassis 102 to provide additional body coverage for wearers ofdiffering size, i.e., to tailor the diaper to an individual wearer. Suchextension of the waist region or regions may give the absorbent articlea generally hourglass shape, so long as the crotch region is extended toa relatively lesser degree than the waist region or regions, and mayimpart a tailored appearance to the article when it is worn.

The diaper 100 may include a backsheet 136. The backsheet 136 may alsodefine the outer surface 134 of the chassis 102. The backsheet 136 maybe impervious to fluids (e.g., menses, urine, and/or runny feces) andmay be manufactured in part from a thin plastic film, although otherflexible liquid impervious materials may also be used. The backsheet 136may prevent the exudates absorbed and contained in the absorbent corefrom wetting articles which contact the diaper 100, such as bedsheets,pajamas and undergarments. The backsheet 136 may also comprise a wovenor nonwoven material, polymeric films such as thermoplastic films ofpolyethylene or polypropylene, and/or a multi-layer or compositematerials comprising a film and a nonwoven material (e.g., having aninner film layer and an outer nonwoven layer). The backsheet may alsocomprise an elastomeric film. An example backsheet 136 may be apolyethylene film having a thickness of from about 0.012 mm (0.5 mils)to about 0.051 mm (2.0 mils). Exemplary polyethylene films aremanufactured by Clopay Corporation of Cincinnati, Ohio, under thedesignation BR-120 and BR-121 and by Tredegar Film Products of TerreHaute, Ind., under the designation XP-39385. The backsheet 136 may alsobe embossed and/or matte-finished to provide a more clothlikeappearance. Further, the backsheet 136 may permit vapors to escape fromthe absorbent core (i.e., the backsheet is breathable) while stillpreventing exudates from passing through the backsheet 136. The size ofthe backsheet 136 may be dictated by the size of the absorbent core 142and/or particular configuration or size of the diaper 100.

Also described above, the diaper 100 may include a topsheet 138. Thetopsheet 138 may also define all or part of the inner surface 132 of thechassis 102. The topsheet 138 may be compliant, soft feeling, andnon-irritating to the wearer's skin. It may be elastically stretchablein one or two directions. Further, the topsheet 138 may be liquidpervious, permitting liquids (e.g., menses, urine, and/or runny feces)to penetrate through its thickness. A topsheet 138 may be manufacturedfrom a wide range of materials such as woven and nonwoven materials;apertured or hydroformed thermoplastic films; apertured nonwovens,porous foams; reticulated foams; reticulated thermoplastic films; andthermoplastic scrims. Woven and nonwoven materials may comprise naturalfibers such as wood or cotton fibers; synthetic fibers such aspolyester, polypropylene, or polyethylene fibers; or combinationsthereof. If the topsheet 138 includes fibers, the fibers may bespunbond, carded, wet-laid, meltblown, hydroentangled, or otherwiseprocessed as is known in the art.

Topsheets 138 may be selected from high loft nonwoven topsheets,apertured film topsheets and apertured nonwoven topsheets. Aperturedfilm topsheets may be pervious to bodily exudates, yet substantiallynon-absorbent, and have a reduced tendency to allow fluids to pass backthrough and rewet the wearer's skin. Exemplary apertured films mayinclude those described in U.S. Pat. Nos. 5,628,097; 5,916,661;6,545,197; and 6,107,539.

As mentioned above, the diaper 100 may also include an absorbentassembly 140 that is joined to the chassis 102. As shown in FIGS. 1A and1B, the absorbent assembly 140 may have a laterally extending front edge148 in the front waist region 116 and may have a longitudinally opposingand laterally extending back edge 150 in the back waist region 118. Theabsorbent assembly may have a longitudinally extending right side edge152 and may have a laterally opposing and longitudinally extending leftside edge 154, both absorbent assembly side edges 152 and 154 may extendlongitudinally between the front edge 148 and the back edge 150. Theabsorbent assembly 140 may additionally include one or more absorbentcores 142 or absorbent core layers. The absorbent core 142 may be atleast partially disposed between the topsheet 138 and the backsheet 136and may be formed in various sizes and shapes that are compatible withthe diaper. Exemplary absorbent structures for use as the absorbent coreof the present disclosure are described in U.S. Pat. Nos. 4,610,678;4,673,402; 4,888,231; and 4,834,735.

Some absorbent core configurations may comprise fluid storage cores thatcontain reduced amounts of cellulosic airfelt material. For instance,such cores may comprise less than about 40%, 30%, 20%, 10%, 5%, or even1% of cellulosic airfelt material. Such a core may comprise primarilyabsorbent gelling material in amounts of at least about 60%, 70%, 80%,85%, 90%, 95%, or even about 100%, where the remainder of the corecomprises a microfiber glue (if applicable). Such cores, microfiberglues, and absorbent gelling materials are described in U.S. Pat. Nos.5,599,335; 5,562,646; 5,669,894; and 6,790,798 as well as U.S. PatentPublication Nos. 2004/0158212 A1 and 2004/0097895 A1.

The diaper 100 may also include elasticized leg cuffs 156 and anelasticized waistband 158. It is to be appreciated that the leg cuffs156 can be and are sometimes also referred to as leg bands, side flaps,barrier cuffs, elastic cuffs or gasketing cuffs. The elasticized legcuffs 156 may be configured in various ways to help reduce the leakageof body exudates in the leg regions. Example leg cuffs 156 may includethose described in U.S. Pat. Nos. 3,860,003; 4,909,803; 4,695,278;4,795,454; 4,704,115; and U.S. Patent Publication No. 2009/0312730 A1.

The elasticized waistband 158 may provide improved fit and containmentand may be a portion or zone of the diaper 100 that may elasticallyexpand and contract to dynamically fit a wearer's waist. The elasticizedwaistband 158 may extend longitudinally inwardly from the waist edges120, 122 of the diaper toward the lateral edges 148, 150 of theabsorbent core 142. The diaper 100 may also include more than oneelasticized waistband 158, for example, having one waistband 158positioned in the back waist region 118 and one waistband 158 positionedin the front wait region 116, although other configurations may beconstructed with a single elasticized waistband 158. The elasticizedwaistband 158 may be constructed in a number of different configurationsincluding those described in U.S. Pat. Nos. 4,515,595 and 5,151,092. Insome configurations, the elasticized waistbands 158 may includematerials that have been “prestrained” or “mechanically prestrained”(subjected to some degree of localized pattern mechanical stretching topermanently elongate the material). The materials may be prestrainedusing deep embossing techniques as are known in the art. In someconfigurations, the materials may be prestrained by directing thematerial through an incremental mechanical stretching system asdescribed in U.S. Pat. No. 5,330,458. The materials are then allowed toreturn to their substantially untensioned condition, thus forming a zerostrain stretch material that is extensible, at least up to the point ofinitial stretching. Examples of zero strain materials are disclosed inU.S. Pat. Nos. 2,075,189; 3,025,199; 4,107,364; 4,209,563; 4,834,741;and 5,151,092.

As shown in FIG. 1B, the chassis 102 may include longitudinallyextending and laterally opposing side flaps 160 that are disposed on theinterior surface 132 of the chassis 102 that faces inwardly toward thewearer and contacts the wearer. Each side flap may have a proximal edge.The side flaps may also overlap the absorbent assembly 140, wherein theproximal edges extend laterally inward of the respective side edges ofthe absorbent assembly 152 and 154. In some configurations, the sideflaps may not overlap the absorbent assembly. It is to be appreciatedthat the side flaps may be formed in various ways, such as for example,by folding portions of the chassis 102 laterally inward, i.e., towardthe longitudinal axis 124, to form both the respective side flaps andthe side edges 128 and 130 of the chassis 102. In another example, theside flaps may be formed by attaching an additional layer or layers tothe chassis at or adjacent to each of the respective side edges and ofthe chassis. Each of the side flaps may be joined to the interiorsurface 132 of the chassis and/or the absorbent assembly in side flapattachment zones in the front waist region 116 and in side flapattachment zones in the back waist region 118. The side flaps may extendto the same longitudinal extent as the absorbent article oralternatively the side flaps may have a longitudinal extent that is lessthan the absorbent article.

Taped diapers may be manufactured and provided to consumers in aconfiguration wherein the front waist region and the back waist regionare not fastened, pre-fastened, or connected to each other as packaged,prior to being applied to the wearer. For example, the taped diaper 100may be folded about a lateral centerline with the interior surface 132of the first waist region 116 in surface to surface contact with theinterior surface 132 of the second waist region 118 without fastening orjoining the waist regions together. The rear side panels 104 and 106and/or the front side panels 108 and 110 may also be folded laterallyinward toward the inner surfaces 132 of the waist regions 116 and 118.

The diaper 100 may also include various configurations of fasteningelements to enable fastening of the front waist region 116 and the backwaist region 118 together to form a closed waist circumference and legopenings once the diaper is positioned on a wearer. For example, asshown in FIGS. 1A and 1B, the diaper 100 may include first and secondfastening members 162, 164, also referred to as tabs, connected with thefirst and second rear side panels 104, 106, respectively. The diaper mayalso include first and second front side panels 108, 110, that may ormay not include fastening members.

With continued reference to FIGS. 1A and 1B, each side panel 104, 106and/or fastening member 162 and 164 may form a portion of or may bepermanently bonded, adhered or otherwise joined directly or indirectlyto the chassis 102 laterally inward from the side edge 128 and 130, inone of the front waist region 116 or the back waist region 118.Alternatively, the fastening members 162, 164 may form a portion of ormay be permanently bonded, adhered or otherwise joined directly orindirectly to the first and second rear panels 104, 106 at or adjacentthe distal edge of the panel and/or the first and second front sidepanels 108 and 110 at or adjacent the distal edge of the side panel. Itis to be appreciated that the fastening members and/or side panels maybe assembled in various ways, such as disclosed for example, in U.S.Pat. No. 7,371,302. The fastening members 162, 164 and/or side panels104, 106, 108, 110 may also be permanently bonded or joined at oradjacent the side edges 128 and 130 of the chassis 102 in various ways,such as for example, by adhesive bonds, sonic bonds, pressure bonds,thermal bonds or combinations thereof, such as disclosed for example,U.S. Pat. No. 5,702,551.

Referring now to FIG. 1B, the first fastening member 162 and/or thesecond fastening member 164 may include various types of releasablyengageable fasteners. The first and second fastening members 162 and/or164 may also include various types of refastenable fastening structures.For example, the first and second fastening members 162 and 164 mayinclude mechanical fasteners, 166, in the form of hook and loopfasteners, hook and hook fasteners, macrofasteners, buttons, snaps, taband slot fasteners, tape fasteners, adhesive fasteners, cohesivefasteners, magnetic fasteners, hermaphroditic fasteners, and the like.Some examples of fastening systems and/or fastening members 162, 164 arediscussed in U.S. Pat. Nos. 3,848,594; 4,662,875; 4,846,815; 4,894,060;4,946,527; 5,151,092; 5,221,274; 6,251,097; 6,669,618; 6,432,098; andU.S. Patent Publication Nos. 2007/0078427 A1 and 2007/0093769 A1.

The fastening members 162 and 164 may be constructed from variousmaterials and may be constructed as a laminate structure. The fasteningmembers 162 and 164 may also be adapted to releasably and/orrefastenably engage or connect with another portion of the diaper 100.For example, as shown in FIG. 1A, the diaper 100 may include aconnection zone 168, sometimes referred to as a landing zone, in thefirst waist region 116. As such, when the taped diaper 100 is placed ona wearer, the fastening members 162 and 164 may be pulled around thewaist of the wearer and connected with the connection zone 168 in thefirst waist region 116 to form a closed waist circumference and a pairof laterally opposing leg openings. It is to be appreciated that theconnection zone may be constructed from a separate substrate that isconnected with the chassis 102 of the taped diaper, such as shown inFIG. 1A. As such, the connection zone 168 may have a pitch length PLdefined by a distance extending between a first lateral end edge 168 aand the second lateral end edge 168 b. In some configurations, theconnection zone may be integrally formed as part of the backsheet 136 ofthe diaper 100 or may be formed as part of the first and second frontpanels 108, 110, such as described in U.S. Pat. Nos. 5,735,840 and5,928,212.

Absorbent articles 100 may also be configured as diaper pants 100Phaving a continuous perimeter waist opening and continuous perimeter legopenings. For example, FIG. 2A shows a perspective view of an absorbentarticle 100 in the form of a diaper pant 100P in a pre-fastenedconfiguration, and FIGS. 2B-2C show front and rear plan views of thediaper pant 100P. The diaper pant 100P may include a chassis 102 such adiscussed above with reference to FIG. 1A and a ring-like elastic belt170 such as shown in FIG. 2A. In some configurations, a first elasticbelt 172 and a second elastic belt 174 are bonded together to form thering-like elastic belt 170. As such, diaper pants may be manufacturedwith the ring-like elastic belt 174 and provided to consumers in aconfiguration wherein the front waist region 116 and the back waistregion 118 of the chassis 102 are connected to each other as packaged,prior to being applied to the wearer. As such, diaper pants may have acontinuous perimeter waist opening 176 and continuous perimeter legopenings 178 such as shown in FIG. 2A.

The ring-like elastic belt 170 may be defined by a first elastic belt172 connected with a second elastic belt 174. As shown in FIGS. 2A-2C,the first elastic belt 172 extends between a first longitudinal sideedge 180 a and a second longitudinal side edge 180 b. And the secondelastic 174 belt extends between a first longitudinal side edge 182 aand a second longitudinal side edge 182 b. The distance between thefirst longitudinal side edge 180 a and the second longitudinal side edge180 b defines a pitch length, PL, of the first elastic belt 172, and thedistance between the first longitudinal side edge 182 a and the secondlongitudinal side edge 182 b defines the pitch length, PL, of the secondelastic belt 174. The first elastic belt 172 is connected with the firstwaist region 116 of the chassis 102, and the second elastic belt 174 isconnected with the second waist region 118 of the chassis 102. As shownin FIGS. 2A-2C, opposing end regions of the first elastic belt 172 areconnected with opposing end regions of the second elastic belt 174 at afirst side seam 184 and a second side seam 186 to define the ring-likeelastic belt 170 as well as the waist opening 176 and leg openings 178.It is to be appreciated that the ring-like elastic belt may be formed byjoining a first elastic belt to a second elastic belt with permanentside seams or with openable and reclosable fastening systems disposed ator adjacent the laterally opposing sides of the belts.

As previously mentioned, absorbent articles may be assembled withvarious substrates that may be inspected during assembly. Thus, in thecontext of the previous discussion, the apparatuses and methods hereinmay be used to inspect substrates and components during the manufactureof an absorbent article 100 to detect various characteristics, such asfor example, wrinkles, missing components, and/or misplaced components.For example, the apparatuses and methods herein may be utilized todetect through holes in any of the topsheet 138; backsheet 136;absorbent core 140; leg cuffs 156; waist feature 158; side panels 104,106, 108, 110; connection zones 168; fastening elements 162, 164, 166,and/or belts during the manufacture of an absorbent article 100. It isto be appreciated that the apparatuses and methods herein may detect thepresence of, size, shape, location, orientation, and/or positions ofholes in various substrates caused by various process operations carriedout on the substrates during a manufacturing process, such as forexample, high pressure bonding, the application of hot adhesives;ring-roll activation, and others.

FIG. 3 shows a schematic representation of an absorbent articleconverting process including a converting line or machine 300 configuredto manufacture absorbent articles 100. It is to be appreciated that thesystems and methods disclosed herein are applicable to work with varioustypes of converting processes and/or machines. As shown in FIG. 3, theconverting line 300 may include one or more motors 302 that drivetransport systems, such as a nip roll 304, to move diaper substrates andcomponent materials through the manufacturing process. For example, FIG.3 shows a base substrate 306 and two auxiliary substrates and/orcomponents 308 of material used to construct portions of the diapers.The substrates may be provided as rolls and fed into the converting line300. It is to be appreciated that material of the auxiliary substratesmay be supplied in various ways. For example, FIG. 3 shows a firstauxiliary substrate 310 in the form of a continuous substrate 312, and asecond auxiliary substrate 314 in the form of individual components 316.It is to be appreciated that the auxiliary substrates 310 may betransferred to the base substrate through various types of transfermechanisms. For example, the individual components 316 may be in theform of side panels 104, 106, 108, 110 such as shown in FIG. 1A. Assuch, the side panels 104, 106, 108, 110 may be transferred to the basesubstrate via a transfer mechanism 318 in the form of a servo patchplacer mechanism 320, such as disclosed in U.S. Pat. Nos. 6,450,321;6,705,453; 6,811,019; and 6,814,217. In addition, the nip roll 304 maybe configured create bonds between the side panels 104, 106, 108, 110and the chassis 102. For example, the nip roll 304 may be configured asa mechanical bonding unit, such as disclosed in U.S. Pat. No. 4,854,984.In another example, the nip roll may be configured as a thermal bondingunit such as disclosed in U.S. Pat. No. 6,248,195. It is also to beappreciated that the various substrates can be used to construct variouscomponents of the absorbent articles, such as backsheets, topsheets,ears, leg cuffs, elastic waist features, and absorbent cores. Exemplarydescriptions of absorbent article components are provided above withreference to FIGS. 1A and 1B.

Referring still to FIG. 3, as the base substrate 306 advances throughthe converting line 300, the base substrate 306 is combined with theauxiliary substrates 308 and/or discrete components 316 to create acontinuous length of absorbent articles 400. At a downstream portion ofthe converting process 300, the continuous length of absorbent articles400 is subjected to a final knife 324 and cut to create separate anddiscrete absorbent articles 100 in the form of diapers. Defectivearticles 100R may be subject to a rejection system 326 and removed fromthe process. For example, FIG. 3 shows defective articles 100R beingchanneled to a reject bin 328. It is to be appreciated that the term“reject bin” is used herein generically to designate the location whererejected diapers may be conveyed. As such, the reject bin 328 mayinclude various systems. For example, the reject bin 328 may includeadditional systems such as conveyors and/or pneumatic systems to provideadditional transport or conveyance of rejected diapers to otherlocations. Articles 100 that are not deemed to be defective may besubject to further processing steps, such as folding and packaging. Forexample, FIG. 3 shows diapers 100 advancing from the final knife 324 toa packaging system 330 and placed into packages 101.

As shown in FIG. 3 an inspection system 600 may be configured tointeract with, monitor, and/or control the converting line 300. Varioussensors 602 and other devices may be arranged adjacent to the convertingline 300 may communicate with a controller 604. As described in moredetail below, a convolutional neural network 650 associated with thecontroller 604 can be utilized to process the communications receivedfrom the various sensors 602. Based on the processing of suchcommunications, the controller 604 may monitor and affect variousoperations on the converting line 300. For example, the controller maysend various types of control commands 1000 to the converter line, suchas disclosed, for example, in U.S. Pat. Nos. 8,145,338; 8,145,344; and8,145,343. In some configurations, the control commands 1000 may be inthe form of reject commands communicated to the reject system 326.

It is to be appreciated that the controller 604 and associatedconvolutional neural network 650 may be configured in various ways. Forexample, the controller 604 may be in the form of a personal computer(PC), a central processing unit (CPU), a field programmable gate array(FPGA), an application specific integrated circuit (ASIC), or agraphical processing unit (GPU). Example hardware with FPGAs may includethe National Instruments PCIe-1473R, National Instruments PXIe-1435,National Instruments 1483R with FlexRIO FPGA module, or individual FPGAdevices from the Altera Stratix, Altera Cyclone, Xilinx Spartan, orXilink Vertexseries. GPU examples may include devices from nVidiaplatforms such as the Titanseries, GeForce GTX7 through 11 series,Quadro or Jetson series, or from AMD's Radeon HD, R, and RX series.

It is to be appreciated that the controller 604 may also be configuredto communicate with one or more computer systems, such as for example, aprogrammable logic controller (PLC), programmable automation controller(PAC), and/or personal computer (PC) running software and adapted tocommunicate on an Ethernet/IP network, or using one or more othersuitable network protocols, such as USB, FireWire and CameraLink. Someconfigurations may utilize industrial programmable controllers such asthe Siemens S7 series, Rockwell ControlLogix, SLC or PLC 5 series, orMitsubishi Q series. The aforementioned configurations may use apersonal computer or server running a control algorithm such as RockwellSoftLogix or National Instruments Labview or may be any other devicecapable of receiving inputs from sensors, performing calculations basedon such inputs and generating control actions through servomotorcontrols, electrical actuators or electro-pneumatic, electrohydraulic,and other actuators. Process and product data may be stored directly inthe controller or may be located in a separate data historian. In someconfigurations, the historian is a simple data table in the controller,in other configurations, the historian may be a relational or simpledatabase. Common historian applications include Rockwell AutomationFactory Talk Historian, General Electric Proficy Historian, OSI PI, orany custom historian that may be configured from Oracle, SQL or any of anumber of database applications. In some configurations, and asdescribed in more detail below, the process and product data may includeimages that are collected by the sensors that can be subsequentlyutilized as training images when determining convolutional neuralnetwork parameters for a convolutional neural network 650. It is also tobe appreciated that the controller 604 may be configured to communicatewith various types of controllers and inspection sensors configured invarious ways and with various algorithms to provide various types ofdata and perform various functions, for example, such as disclosed inU.S. Pat. Nos. 5,286,543; 5,359,525; 6,801,828; 6,820,022; 7,123,981;8,145,343; 8,145,344; and 8,244,393; and European Patent No. EP 1528907B1, all of which are incorporated by reference herein.

As the substrates and components travel in the machine direction (MD)through the converting line, the controller 604 tracks the advancementof the substrates and components. In some configurations such as shownin FIG. 3, the controller 604 may track the advancement with countsgenerated by a machine axis 332 that correspond with machine directionpositions on substrates and components while advancing though theconverting line 300. In some configurations, the machine axis 332 may beconfigured as an actual motor 302 that provides count signals 1002 tothe controller 604. The controller 604 may utilize rotational speed,time, and/or count data from the machine axis 332 that correspond withthe machine direction speed and travel of the substrates and componentsthrough the converting line 300.

It is to be appreciated that instead of or in addition to utilizingfeedback from a physical machine axis as discussed above, the rotationalmotion of the machine axis 332 may be simulated by software in thecontroller. For example, in FIG. 3, the controller 604 can utilizecounts generated by a virtual machine axis 334 in the controllersoftware. More particularly, the virtual machine axis 334 may beprogrammed to imitate a motor that generates counts as the motorrotates. As such, it is to be appreciated that the machine axis 332referred to herein may be either a virtual axis existing in software ora physical axis corresponding with the rotational motion of a motor orother equipment.

As discussed above, the machine axis 332 may be configured to correlatethe linear motion of the substrates and components in the machinedirection through the converting line 300 with counts corresponding withrotation of the machine axis 332. In some configurations, one completerotation of the machine axis 332 and associated count data correspondwith one pitch length of an absorbent article 100. In someconfigurations, the pitch lengths of the absorbent articles are themachine direction longitudinal lengths of the individual absorbentarticles being produced. FIGS. 1A and 1B show an example of alongitudinal pitch length PL of a diaper. As such, the controller 604may use counts generated from the machine axis 332 to virtually dividethe substrates and components into virtual products 402. As shown inFIG. 4, the virtual products 402 may have machine direction lengths PLthat correspond with the pitch lengths PL of products being produced.For example, FIG. 4 shows a top view of the base substrate 306 dividedinto virtual products 402 along the machine direction by the controller604. Count signals corresponding with rotation of the machine axis thatcorrespond with less than a complete rotation can also be used by thecontroller to divide each virtual product 402 into virtual segments 404,such as shown in FIG. 4. As discussed in more detail below, thesubstrate speed and estimated clock inaccuracies can be used todetermine the length of each virtual segment in the machine direction,and in turn, the number of virtual segments in each virtual product. Forexample, FIG. 4 shows one virtual product 402 divided into twentyvirtual segments 404. As discussed in more detail below, the controller604 can also utilize signals from the sensor 602 that correspond withthe detection of various parameters in virtual products and segments tocorrelate the locations of parameters within manufactured products 100based on processing of the signals by the convolutional neural network650. It is also to be appreciated that the controller 604 may beconfigured to correlate inspection results and measurements from anabsorbent article converting process using any of a variety of suitabletechniques, such as those disclosed in U.S. Pat. No. 8,145,338, which isincorporated by reference herein.

As previously mentioned, the systems and methods herein utilize varioustypes of sensors 602 to monitor the substrates and components travelingthrough the converting line. As shown in FIG. 3, sensors 602 may beconfigured as inspection sensors 606 to monitor various aspects in thesubstrates and/or components being processed by the converting line ormachine 300. In some configurations, the inspection sensors 606 may beused to assist with the detection of defects within substrates and/orcomponents themselves, such as for example, damage, holes, tears,wrinkles, and the like, and may also be used to assist with thedetection of defective assemblies and/or combinations of the substratesand components, such as for example, missing and/or misplaced ears,landing zones, fasteners, and the like. The inspection sensors 606 maybe used to assist with the detection of contaminants, such as foreignmaterial, grease, or dirt. As such, inspection sensors 606 may beconfigured to assist with the detection of the presence or absence ofsubstrates and/or components, and may be configured to assist with thedetection of the relative placement of substrates and/or components. Asdiscussed in more detail below, feedback signals from the inspectionsensors 606 in the form of inspection parameters 1006 are communicatedto the controller 604 for processing by an inspection algorithm of theconvolutional neural network 650. In some configurations, for instance,the inspection parameters 1006 include images of the base substrate 306,the auxiliary substrates 308, discrete components 316, and so forth. Insome configurations, such images can be collected by the inspectionsensors 606 without needing to illuminate the area of inspection withlight with required specific wavelengths, such as infrared light and/orultraviolet light. Additionally or alternatively, the inspectionparameters 1006 can include other forms of data, such as digitizedsignals, that can be processed by the inspection algorithm of theconvolutional neural network 650.

It is to be appreciated that various different types of inspectionsensors 606 may be used to monitor substrates and various componentswhile advancing through the converting line 300. For example, inspectionsensors 606 may be configured as photo-optic sensors that receive eitherreflected or transmitted light and serve to determine the presence orabsence of a specific material; metal-proximity sensors that useelectromagnetic to determine the presence or absence of a ferromagneticmaterial; or capacitive or other proximity sensors using any of a numberof varied technologies to determine the presence, absence, or thicknessof materials. In some configurations, the inspection sensors 606 mayalso be configured as vision systems and other sub-processing devices toperform detection and, in some cases, logic to more accurately determinethe status of an inspected product. Particular examples of suchinspection sensors 606 may include the Sick PS30 pattern sensor, KeyenceAI series pattern matching sensor, Cognex Insight cameras, DVT Legend orKeyence smart cameras, component vision systems such as NationalInstruments PXI or PC based vision system such as Cognex VisionPro orany other vision system software which can run on a PC platform. Itshould also be appreciated that inspection parameters 1006 may beprovided from inspection sensors 606 in various forms. In oneconfiguration, inspection parameters 1006 may be in the form of imagesor other types of signals which are processed by the convolutionalneural network 650 to determine the presence or absence of a particulardefect, feature and/or other component. Such images or signals may alsobe stored in any suitable data store, such as, for example, a databaseor in a specified directory on an image server for offline processing inthe refinement of the inspection algorithm of the convolutional neuralnetwork 650, as described below. Such images or signals can betransferred via a standard protocol such as ftp (File TransferProtocol), GigE, 10 GbE, TCPIP, USB, CameraLink, CoaXpress, Thunderbolt,N-BaseT, 10G Base-T, HD-SDI. NFS (network file system), SMB (ServerMessage Block), DDE (Dynamic Data Exchange), or OPC (Object Linking andEmbedding for Process Control).

The systems and methods herein utilize various types of sensors 602 ordata from the controller 604 to monitor the various assembly equipmentused in the converting line 300. As shown in FIG. 3, equipment sensors602 may be configured as process sensors 608 to monitor various aspectsof process equipment or operations. In some configurations, the processor equipment sensors may be linear position transmitters, rotaryposition transmitters, rotational encoders for speed and positionfeedback, temperature sensors such as RTD elements, pressure and/orvacuum transmitters or vibration sensors. Controller data may beconfigured as data from drive position or velocity control loops,automatic or operator induced control actions, motor current or power orany other parameter that can be harvested from a controller 604. Basedon the detections of the process sensors 608, feedback signals from theprocess sensors 608 in the form of process parameters 1008 arecommunicated to the controller 604.

As shown in FIG. 3, the sensors 602, such as the inspection sensors 606and process sensors 608, may be connected with the controller 604 andhistorian through a communication network 614, which allows theinspection sensors 606 and process sensors 608 to communicate inspectionparameters 1006 and process parameters 1008, respectively, to thecontroller 604. As discussed in more detail below, devices thatcommunicate on the network each include precision clocks that aresynchronized to a master clock within some specified accuracy. As shownin FIG. 3, the sensors 602 and the controller 604 may be connecteddirectly with the communication network 614. As such, each sensor orother field device connected directly with the communication network mayinclude a clock. Sensors 602 that include a clock and that may beconnected directly with the communication network 614 may include, forexample, vision systems such as National Instruments CVS or any PC-basedvision system such as Cognex VisionPro. Such sensors may also includeother controllers that may be configured as peers to the controller ormay be configured as subordinate to the controller.

In some configurations, the sensors 602, such as the inspection sensors606 and process sensors 608, may be indirectly connected with thecommunication network 614. For example, the inspections sensors 602 maybe connected with the communication network 614 through a remote inputand output (I/O) station 616. When utilizing remote I/O stations 616,the sensors 602 may be hardwired to the remote I/O stations, and inturn, the remote I/O stations are connected with the communicationnetwork 616. As such, each remote I/O station 616 may include aprecision clock. Example remote I/O stations 616 or other IEEE-1588based instruments that can be utilized with systems and methods hereininclude, for example a National Instruments PCI-1588 Interface (IEEE1588 Precision Time Protocol Synchronization Interface) thatsynchronizes PXI systems, I/O modules and instrumentation overEthernet/IP or a Beckhoff Automation EtherCat and XFC technology(eXtreme Fast Control Technology).

As previously mentioned, each device, such as the inspection sensors 606and process sensors 608, remote I/O stations 616, and the controller604, connected with the communication network 614 includes a clock, andeach clock is synchronized to a master clock. In one configuration, thecontroller 604 includes the master clock, and all other clocks ofdevices connected with the communication network are referenced to thecontroller master clock. In such a configuration, the remote I/Ostations, inspection sensors, and process sensors each include a clockthat is synchronized to the controller master clock. For example,inspection parameters 1006 provided by the inspection sensors 606 andprocess parameters 1008 provided by the process sensors 608 communicatedto the communication network 614 are time-stamped with the time from theclocks on the corresponding sensors and remote I/O stations. In turn,the inspection parameters and process parameters, and correspondingtime-stamp data are sent to the controller 604 over the communicationnetwork 614. Thus, the controller 604 can be programmed to correlate theinspection parameters and process parameters based on the actual timethe parameters were provided by the respective sensors. Therefore,ambiguity as to when detections were actually made by respective sensorsis relatively small. Additionally, traditional methods of storinginspection parameters and process parameters normally rely on OPC(Object Linking and Embedding for Process Control) to pass data which issubsequently time-stamped at the destination, for example, a computerhousing the historian. With these methods, the transport delays betweenthe data source and the clock drift of the computer housing thehistorian combine to create further ambiguity in the detectiontime-stamp of the data.

The controller may ‘normalize’ the time-stamps by adjusting the reportedtime-stamps which were recorded at the time of detection to a referencelocation in the process. In this manner, all data may be correlated tothe production time (normalized time) of the particular product on whichthe measurement was detected. For example, if an inspection is performedusing an inspection system 600, which may include a vision systemutilizing an inspection algorithm of the convolutional neural network650, at some location in the process, and equipment parameters arerecorded by a process sensor 602 at a second location in the process,the controller may adjust each time-stamp in such a way that allparameters will have the same time-stamp and therefore be correlated tothe same individual product. Further, if some product is removed fromthe production in order to perform offline manual inspections, thesystem can be configured to record the sample time of the product beingremoved, to adjust that time-stamp to the normalized time of thatindividual product and to present that time-stamp to the qualityassurance laboratory, who may use that time-stamp when that data isstored in the historian. By recording the time-stamp at the moment ofdetection, normalizing it to a reference point in the process andpassing the normalized time-stamp to the historian as the associateddata time-stamp, the majority of the ambiguities in the system areeliminated.

All clocks that are used to determine and report time-stamps may besynchronized together. Clock synchronization allows the reported timefrom one device on the communication network 614 to be utilized byanother device on the communication network. When the clocks aresynchronized, ambiguity as to when parameters were actually provided bythe respective sensors 602 is affected only by the accuracy of theclocks with respect to each other. The clocks of the devices on thecommunication network may be synchronized in various ways depending onthe type of communication network 614 used.

In one configuration, the communication network 614 is configured as anon-deterministic communication network, such as for example, Ethernetor Ethernet IP (industrial protocol) communication network. When usingan Ethernet IP communication network, the clocks of each device may besynchronized using the IEEE1588 precision time protocol, described inIEEE1588 Standard, “Precision Clock Synchronization Protocol forNetworked Measurement and Control Systems” and also described inRockwell Automation publication number 1756-WPO05A-EN-E, publishedJanuary 2009, and entitled “An Application of IEEE 1588 to IndustrialAutomation.” Alternatively, when using an Ethernet communicationnetwork, the clocks of each device may be synchronized via timesensitive networking (TSN) protocols as described by the standard IEEE802.1AS-2011, “IEEE Standard for Local and Metropolitan AreaNetworks—Timing and Synchronization for Time-Sensitive Applications inBridged Local Area Networks.” As mentioned above, time-stamps associatedwith parameters from any sensor may be referenced to the master clock,which allows the relative time as to when the inspection parameters wereprovided to be accurately calculated. In one configuration, thecontroller includes the master clock, the controller master clock, andall other clocks of devices connected with the communication network,the sensor clocks, are referenced to the controller master clock. As aresult, the time as to when inspection parameters, process parameters,and identifier parameters were provided from respective sensors can becan be reported to the controller within the accuracy of a standardscompliant clock. In some configurations, reported time-stamps may beaccurate to within 0.1 milliseconds of the controller master clock. Inanother configuration, another device, such as an Ethernet switch orrouter is the local master clock. In this case, both the controllerclock and the sensor clock follow the local master clock. The identityof the local master is unimportant since all clocks in the system aresynchronized to the local master within the standard.

With reference to the above description and figures, the methods andsystems herein utilize a controller 604 and one or more sensors 602,such as inspection sensors 606 and process sensors 608, connected with acommunication network 614. Each sensor 602, and remote I/O device 616,if used, have clocks that are synchronized with the master controllerclock in the controller. The controller 604 tracks the movement of thesubstrates and components traveling in the machine direction of theconverting line 300. More particularly, controller 604 utilizes feedbackfrom the machine axis 332 to virtually divide the substrates andcomponents into virtual products 402 along the machine direction, trackthe movement of virtual products 402 in the machine direction, andcorrelate the virtual products 402 to actual individual products 100produced after being cut by the final knife 324. In addition, thecontroller 604 utilizes feedback from the machine axis 332 to virtuallydivide the virtual products 402 into virtual segments 404 along themachine direction.

During manufacture, the inspection sensors 606 provide inspectionparameters 1006 to the controller 604 via the communication network 614.As discussed above, the inspection parameters 1006 can be configured toindicate various types of information, such as measurement data and/orimages, about the substrates and/or components. The inspection sensors606 can provide inspection parameters 1006 to the communication networkalong with associated time-stamp from the sensor clocks. Similarly, theprocess sensors 608 can provide process parameters 1008 to thecontroller 604 via the communication network 614. As discussed above,the process parameters 1008 can be configured to indicate various typesof information, such as temperatures and/or pressures, from the assemblyequipment on the converting line 300. In turn, the process sensors 608provide process parameters 1008 to the communication network along withassociated time-stamp from the sensor clocks. The controller 604receives the inspection parameters 1006 and process parameters 1008, andassociated time-stamps from the communication network 614 and correlatesthe inspection parameters 1006 and process parameters 1008 with thecorresponding virtual products 402 and/or virtual segments 404 movingalong the converting line 300, and in turn, with individual products 100in a package 101.

It should be noted that while time-stamps, and specifically normalizedtime-stamps are an efficient method to provide correlation betweenprocess data, inspection parameters and product performance feedback,other techniques to make the correlation may be used. For example, aproduct's unique identifier may be a mathematical sequence. Thecontroller 604 and inspection devices 616 may independently generate thesame sequence. When data is stored from varied sources, each piece ofdata is identified by the product unique identifier rather than a time.

As described in more detail below, based on the inspection algorithm ofthe convolutional neural network 350, the controller 604 may be adaptedto send various types of control commands 1000 to the converting line300, such as for example, speed change commands, reject commands, andshutdown commands. Additionally or alternatively, the control commands1000 can causes various control actions, such as an automatic phaseadjustment, an automatic tracking adjustment, a machine maintenancescheduling, and machine stop commands. Such control commands 1000 may bebased on parameters communicated from various sensors 602 as describedabove. For example, control commands 1000 may be based on imagesincluded in the inspection parameters 1006 and/or process parameters1008 provided by inspection sensors 606 and process sensors 608.

The convolutional neural network 650 can comprise one or moreconvolutional layers, activation layers and pooling layers, followed byone or more fully connected layers as in a standard multilayer neuralnetwork. The convolutional neural network may also be made fullyconvolutional by not including a fully connected layer. In order for theconvolutional neural network 650 to process the images, or other data,collected by the sensors 602, a learning paradigm is utilized todetermine convolutional neural network parameters for an inspectionalgorithm. Once the parameters have been determined, the convolutionalneural network can be used in production to produce resultsindependently using the inspection algorithm. FIG. 5 is a block diagramof a convolutional neural network learning paradigm for determiningconvolutional neural network parameters 660 for an inspection algorithm652 of a convolutional neural network 650 for converting lineinspection. Training can take on many different forms, using acombination of learning paradigms, learning rules, training frameworks,and learning algorithms, which are schematically represented as trainingalgorithm 708 in FIG. 5. The training algorithm 708 can be executed by aprocessor 710 of a training computing system 700. In this configuration,a static network is utilized, as the learning phase is distinct from theproduction phase. As described in more detail below, however, once theconvolutional neural network is deployed into production, additionallearning/training can be utilized (i.e., offline from production) tofine-tune, calibrate, or otherwise adjust the convolutional neuralnetwork parameters. The convolutional neural network can then be updatedwith the refined parameters to improve performance of the inspectionalgorithm.

The learning paradigm for determining a set of convolutional neuralnetwork parameters 660 shown in FIG. 5 is based on the analysis ofimages in a training image database 706. In the illustrated example, theimages stored in the training image database 706 can include a multitudeof images of collected from one or more converting lines 300 duringproduction of absorbent articles 100 (FIGS. 1A and 1B). The images inthe training image database 706 can be tagged or otherwise grouped intotraining sets, such that each image has a label corresponding to theresult that an inspection algorithm is expected to match. For ease ofillustration, the images of the training image database 706 are shown toinclude a set of preprocessed images 702. As such, each of the images702 in the training image database 706 can include a labeled componentthat is of interest for quality control purposes. The labeled componentcan be, for instance, a feature, aspect, or attribute of the absorbentarticle that is marked and tagged as such. The labeled components can becomponents that are desired to be present or components that areundesirable to be present. For instance, the labeled component may markthe landing zone of each image 702 or may mark a wrinkle, tear, orfolded component in each image 702.

With specific regard to landing zones, as an example, detection ofabsorbent article landing zones using traditional machine vision toolspresents challenges. For instance, landing zones may have generally lowcontrast and positioned within areas of busy artwork. Such arrangementscan be challenging for traditional machine vision tools, as the artworkmay be identified as an edge of the landing zone, while the actual edgeof the landing zone is overlooked. In accordance with the presentdisclosure, however, each of the preprocessed images 702 can have thelanding zones labeled such that the inspection algorithm can learn toidentify the landing zone with a much higher degree of accuracy.

Component labeling can be performed using any suitable process ortechnique, such as through human determination, by an artificialintelligence algorithm, a traditional detection algorithm (i.e.,non-artificial intelligence), or other resource intensive, high accuracydetection algorithm, for example. Furthermore, while images are depictedas the data set in FIG. 5, this disclosure is not so limited. Instead,any data set can be provided to the training algorithm 708 that is basedon data that can be collected and digitized by the sensors 602 duringthe manufacturing process for processing by the inspection algorithm652. In this regard, example data sets useable to train theconvolutional neural network 650 can include, without limitation, a 3-Dpoint cloud, height maps, density data, opacity data, aural data,profile signals, sensor data, and so forth.

Once the training computing system 700 has applied the trainingalgorithm 708 to the images in the training image database 706, theconvolutional neural network parameters 660 can be generated. Exampleparameters can include, without limitation, the number of convolutionallayers, the number of neurons per layer, the number of pooling layers,the weights connecting different layers, number of drop out layers, andso forth. The convolutional neural network parameters 660 can then beimplemented as an inspection algorithm 652 of the convolutional neuralnetwork 650 associated with a converting line 300. Based on thedetermination of the inspection algorithm 652 of the convolutionalneural network 650 control commands 1000 can be sent to the to theconverting line 300, as may be needed.

Referring to FIG. 3, the inspection parameters 1006, such as images,received from the sensors 606 can be processed by the inspectionalgorithm 652 to identify the labeled component in each of the absorbentarticle 100. By way of example, if the labeled component is a landingzone, the landing zone of each absorbent article 100 can be identifiedin the images using the inspection algorithm 652. In someconfigurations, the images collected from the converting line 300 can beprocessed using the inspection algorithm 652 such that the landing zoneis identified as white pixels while the remaining pixels of the imageare black. For the purposes of illustration, FIG. 9 shows an example setof images from a converting line in which the landing zone has beenidentified in accordance with the present disclosure. Images 900A-L areimages collected by a sensor, such as sensor 602 shown in FIG. 3. Theimages 900A-L may be processed by an inspection algorithm, such asinspection algorithm 652, to produce the corresponding images 902A-L.The landing zones in the corresponding images 902A-L, if present, areidentified as white pixels. While FIG. 9 depicts images related to thedetection of a single component (i.e., a landing zone), in otherconfigurations multiple components can be labeled for identification.For such cases, a collection of masked images may be produced with eachimage labeling one of the multiple components using black and whitepixels, as shown in FIG. 9. Alternatively, instead of utilizing multipleimages, a single output image can be generated with the differentcomponents separated out on different color channels of the image. Suchimages may then be analyzed directly or separated into individual imagesfrom each channel for analysis.

Again referring to FIG. 9, with the landing zone identified as whitepixels, image analysis can then be performed using a second inspectionalgorithm to determine certain properties of the component. Based on theproperties, it can be determined whether the landing zone satisfiescertain quality control parameters. While the properties of the labeledcomponent will vary based on the labeled component that is identified inthe images, in the instance of a landing zone, the properties may be thetotal area of the landing zone, machine and cross machine position ofthe landing zone, and the angular orientation of the landing zone. Byway of example, if the total area of the landing zone is determined tobe less than a threshold, the landing zone is outside of a desiredposition envelope, and/or the angular orientation of the landing zoneexceeds a certain threshold, the absorbent article can be deemed to bedefective. Referring to image 902I of FIG. 9, the landing zoneidentified in that image may exceed the angular orientation threshold.The position of the landing zone identified in image 902H may be tooclose to the image boundary. The area of the landing zone identified inimage 902L may be less than the threshold landing zone area. In allinstances, the associated absorbent article associated with those imagesmay be rejected based on the assessment of the second inspectionalgorithm.

While the images 900A-L show example processing a landing zone, it is tobe appreciated that a wide variety of labeled components can beidentified using the systems and methods disclosed herein. For instance,the labeled component may be contaminants such as a foreign material,grease, or dirt, or results of an undesired transformation of theabsorbent article, such as a hole, tear, or wrinkle. The image showingthe labeled component can be processed such that the labeled componentis presented as white pixels, with the remaining pixels of the imagebeing black. Based on the properties of the labeled component (presence,absence, total number, length, width, texture, size, position,orientation, etc.), as may be determined through image analysis, variousprocessing decisions can be made.

Referring now to FIG. 6, a block diagram of a convolutional neuralnetwork learning paradigm for determining convolutional neural networkparameters 660 for an inspection algorithm 652 utilizing a feedbackcontrol loop from the converting line 300 is depicted. In thisconfiguration, images of the training image database 706 include imagesof absorbent articles 100 collected by the sensors 606 of the convertingline 300 (FIG. 3) during production and collected while a particular setof convolutional neural network parameters 660 is driving the inspectionalgorithm 652. In some configurations, the production-based imagescollected from the converting line 300 can be processed at an imageprocessing function 762 to label images for utilization by the trainingalgorithm 708. Notably, as the images stored in the line image database760 are collected based on a particular set of convolutional neuralnetwork parameters 660, the efficiency of such parameters can be checkedand verified by the feedback loop. Mischaracterizations or otherprocessing issues can be considered by the training computing system 700such that adjustments to the convolutional neural network parameters 660can be determined by the training computing system 700. Once theconvolutional neural network parameters 660 have be adjusted, they canbe utilized by the inspection algorithm 652 to improve the accuracy andfunctionality of the inspection process.

With regard to visually detectable features or conditions of theabsorbent article, the location of the feature or conditions inproximity to other visual stimulus may impact the visual perception tothe consumer. More specifically, the location of the features orconditions may make them more obvious to an observer, or they may becomemore difficult to notice. For instance, the particular location of adefect or condition may impact whether the article should be rejected,as some defects or issues may not necessarily impact visual perceptionto such a degree that warrants the article being rejected. Therefore, insome configurations, the inspection algorithm 652 can be calibrated toidentify the presence or absence of a certain defect, feature, orcondition based on saliency modeling. As such, the inspection algorithm652 can therefore be calibrated to reject articles only having defects,features, or conditions that impact the visual perception of thearticle. Beneficially, when utilizing saliency modeling, the large size(i.e., complexity) of the convolutional neural network 650 can bereduced. Reducing the number of layers of the convolutional neuralnetwork 650 can decrease processing time and can also reduce thecomputing resources necessary to execute the convolutional neuralnetwork 650.

Referring now to FIG. 7, the training computing system 700 is depictedutilizing saliency filtering 720 to filter the pre-processed images 702such that the training algorithm 708 will determine the convolutionalneural network parameters 660 that take into account a consumer's visualperception of the defect or condition. Subsequent to the saliencyfiltering 720, the filtered images 722 can be stored in the trainingimage database 706 and used by the training algorithm 708 to determinethe convolutional neural network parameters 660.

The saliency filtering 720 can be performed using any suitabletechnique. In one example configuration, the saliency of thepre-processed images 702 is provided as additional channels to the inputof the training algorithm 708. More specifically, the training algorithm708 can provide the ability to train on color channel inputs, as opposedto just grayscale images. The saliency data of the pre-processed images702 can be embedded in the filtered images 722 by creating a color imagefrom a combination of the original grayscale image and the saliencyheat-map. In accordance with one configuration, the saliency filtering720 creates the filtered images 722 in the HSL(hue-saturation-luminance) color space which are then stored in thetraining image database 706. However, other color spaces, such as an RGBcolor space or a LAB color space, can be used. Using the HSL color spacecan ensure that the original grayscale data (i.e., pre-processed images702) are fully recoverable from the color image (filtered images 722)without risk of inducing false saturation or false clipping during theconversion process. Using the HSL color space, the saliency filtering720 can import the original grayscale pre-processed image 702 as theluminance channel, and the saliency map can be imported as the huechannel. The saturation channel can be set as a percentage of precept,where 100% saturation provides the full strength of the saliency inputand 0% effectively ignores the saliency input. Utilizing a variablesaturation channel beneficially allows certain regions of an image setto be masked (i.e., 0% saturation), if desired, so that only theremaining regions are considered by the training algorithm 708.

Providing saliency input to the training algorithm 708 can serve toreduce the number of convolutional layers the network has to train.Accordingly, the convolutional neural network parameters 660 arereduced, thereby allowing the convolutional neural network 650 (FIG. 5)to fit onto suitable hardware device(s) providing local and fastprocessing ability, such as a FPGA or the like.

Referring now to FIG. 8, in some implementations, the training computingsystem 700 can be positioned in a non-production environment 780. Thenon-production environment 780 can be in networked communication withone or more production environments 782, 784, 786 via a network 788.Each of the production environments can have one or more convertinglines similar to the converting line 300 shown in FIG. 3. At certainintervals, or at an on-demand basis, updated convolutional neuralnetwork parameters 660 determined by the training computing system 700can be transmitted to the various converting lines 300A-D via networkcommunications in order to update or otherwise alter the inspectionalgorithm associated with those converting lines. For instance, theconvolutional neural network parameters 660 can be written to thecontrollers implementing the inspection algorithm at the variousconverting lines. Thus, the converting lines 300A-D can be operated witha particular inspection algorithm while the training computing system700 determines improved parameters for the associated convolutionalneural network 650 (FIG. 5). Once determined, the convolutional neuralnetwork parameters 660 can be dispatched to the various remoteconverting lines 300A-D. Further, some or all of the converting lines300A-D can supply some or all of the images 702 to the trainingcomputing system 700 in a feedback loop, as shown in FIG. 6. Thus,training images collectively received from many different convertinglines 300A-D can be utilized by the training algorithm 708 to improvethe convolutional neural network parameters 660. As is to beappreciated, the training computing system 700 positioned in anon-production environment 780 can utilize saliency filtering 720 (FIG.7) to pre-process the images received from the converting lines 300A-D.

Converting lines utilizing convolutional neural networks describedherein can provide for a variety of different inspection operations todiscern consumer noticeable defects during the manufacturing process.Examples of features, flaws, defects, or other issues that can bedetected using the systems and methods described herein include holes ortears in substrates, missing components, misplaced components,deformations of components, sufficiency of textures, bond location, bondappearance, and so forth. Various operational examples are providedbelow for illustration purposes.

In accordance with one non-limiting configuration, the inspectionalgorithm 652 of convolutional neural network 650 is used to discern theedge of a side panel 104 of an absorbent article 100 (FIGS. 1A and 1B)from bond patterns, graphics, textures, and other visual indicia thatmay be present during the inspection. Beneficially, the inspectionalgorithm 652 can determine whether the placement of the side panel 104is acceptable with white light and without having to use light having aspecific wavelength, such as infrared and/or ultraviolet light. In orderto perform this type of inspection, a plurality of images 702 labeled ashaving proper placement of the side panel can be provided as an input tothe training algorithm 708. In some implementations, the pre-processedimages 702 can be filtered with saliency filtering 720, as describedabove. In any event, the training algorithm 708 then determines theconvolutional neural network parameters 660 based on the pre-processedimages 702. A convolutional neural network 650 utilizing theconvolutional neural network parameters 660 can then process theinspection parameters 1006 from one or more sensors 602 to discern theplacement of an edge of the side panel 104 during production.

In accordance with another non-limiting configuration, the inspectionalgorithm 652 of convolutional neural network 650 is used to detect anelastic strand so as to differentiate the elastic strand from a wrinkle.Similar to the above, such inspection can be performed in the presenceof bond patterns, graphics, and other visual indicia that may causeconfusion for other types of vision inspection systems. In order toperform this type of inspection, a plurality of pre-processed images 702that label wrinkles can be provided as an input to the trainingalgorithm 708. The pre-processed images 702 can be filtered withsaliency filtering 720, as described above. The training algorithm 708can then determine the convolutional neural network parameters 660 basedon the pre-processed images 702. A convolutional neural network 650utilizing the convolutional neural network parameters 660 can thenprocess inspection parameters 1006 from one or more sensors 602 todiscern the elastic strand from a wrinkle during production.

Combinations

A. A method for inspecting absorbent articles, the method comprising thesteps of: preprocessing each image of a group of images of absorbentarticles to label a component of the absorbent article; generating afirst inspection algorithm with a convolutional neural network based onthe preprocessed images, wherein the first inspection algorithm isusable to mask the labeled component in images of absorbent articles;providing a second inspection algorithm, wherein the second inspectionalgorithm is usable to determine one or more properties of the labeledcomponent in images of absorbent articles, wherein the one or moreproperties comprise any of a location, a size, a shape, and a presenceof the component; providing a communication network; connecting a sensorwith the communication network; connecting a controller with thecommunication network, the controller comprising the first inspectionalgorithm; advancing a substrate through a converting process;sequentially adding component parts to the substrate; creating images ofat least one of the substrate and component parts with the sensor;communicating the images from the sensor to the controller; masking thelabeled component in the images by analyzing the images with the firstinspection algorithm; determining at least one of the properties for thelabeled component in the substrate with the second inspection algorithm;cutting the substrate with component parts added thereto into discreteabsorbent articles; and based on the determination of the least one ofthe properties for the labeled component in the substrate, executing acontrol action, wherein the control action is any of rejecting one ormore of the discrete absorbent articles, an automatic phase adjustment,an automatic tracking adjustment, a machine maintenance scheduling, anda machine stop command.B. The method according to paragraph A, wherein the controller comprisesany of a field programmable gate array, a graphics processing unit, anda central processing unit.C. The method according to paragraphs A-B, wherein the component partsinclude parts added as a continuous web of material and parts added as adiscontinuous web of material.D. The method according to paragraphs A-C, wherein the communicationnetwork comprises any of an Ethernet protocol, a USB protocol, aFireWire protocol, and a CameraLink protocol.E. The method according to paragraphs A-D, wherein the labeled componentcomprises any of a contaminant and an undesirable transformation of theabsorbent article.F. The method according to paragraph E, wherein the undesirabletransformation of the absorbent article comprises any of a hole, a tear,and a wrinkle.G. The method according to paragraphs A-F, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises determining an angularorientation of the labeled component.H. The method according to paragraphs A-G, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises determining the absenceof the labeled component.I. The method according to paragraphs A-H, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises determining a texture ofthe labeled component.J. The method according to paragraphs A-I, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises determining a totalnumber of the labeled components.K. The method according to paragraphs A-J, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises determining a deformationof the labeled component.L. The method according to paragraphs A-K, wherein the absorbentarticles are any of diapers and feminine hygiene products.M. The method according to paragraph L, wherein the component partscomprise any of ears, tapes, graphics, elastics, core, cuts, and/orwrappers.N. A method for inspecting absorbent articles, the method comprising thesteps of: preprocessing each image of a group of images of absorbentarticles to label a component of the absorbent article;—generating afirst inspection algorithm with a convolutional neural network based onthe preprocessed images, wherein the first inspection algorithm isusable to mask the labeled component in images of absorbent articles;providing a second inspection algorithm, wherein the second inspectionalgorithm is usable to determine one or more properties of the labeledcomponent in images of absorbent articles, wherein the one or moreproperties comprise any of a location, a size, a shape, and a presenceof the component; advancing a substrate through a converting process;sequentially adding component parts to the substrate; creating images ofa substrate and one or more component parts; masking the labeledcomponent in the images by analyzing the images with the firstinspection algorithm; and determining at least one of the properties forthe labeled component in the substrate with the second inspectionalgorithm.O. The method according to paragraph N, further comprising cutting thesubstrate with component parts added thereto into discrete absorbentarticles; and based on the determination of the least one of theproperties for the labeled component in the substrate, executing acontrol action.P. The method according to paragraph O, wherein the control action isany of rejecting one or more of the discrete absorbent articles, anautomatic phase adjustment, an automatic tracking adjustment, a machinemaintenance scheduling, and a machine stop command.Q. The method according to paragraphs N-P, wherein determining the atleast one of the properties for the labeled component in the substratewith the second inspection algorithm comprises any of determining anangular orientation of the labeled component, determining the absence ofthe labeled component, determining a texture of the labeled component,determining a total number of the labeled components, determining thepresence of a contaminant, and determining an undesirable transformationof the labeled component.R. A method for inspecting absorbent articles, the method comprising thesteps of: storing a group of digitized signals of absorbent articles,wherein the digitized signals are preprocessed to label a component ofthe absorbent article; providing the preprocessed digitized signals to atraining algorithm to create a convolutional neural network; generatinga first inspection algorithm with the convolutional neural network basedon the group of preprocessed digitized signals, wherein the firstinspection algorithm masks one or more properties of the labeledcomponent; providing a second inspection algorithm, wherein the secondinspection algorithm is usable to determine one or more properties ofthe labeled component in digitized signals of absorbent articles,wherein the one or more properties comprise any of a location, a size, ashape, and a presence of the component; advancing a substrate through aconverting process; sequentially adding component parts to thesubstrate; creating inspection data of at least one of the substrate andcomponent parts with a sensor; communicating the inspection data fromthe sensor to a controller, wherein the controller comprises the firstinspection algorithm; identifying the labeled component in the substrateby analyzing the inspection data with the first inspection algorithm;determining at least one of the properties for the labeled component inthe substrate with the second inspection algorithm; and cutting thesubstrate with component parts added thereto into discrete absorbentarticles.S. The method according to paragraph R, further comprising the step ofexecuting a control action based on the determination of the least oneof the properties for the labeled component in the substrate, whereinthe control action is selected from the group consisting of: adjustingan advancement speed of the substrate; adjusting a placement ofcomponent parts; and rejecting absorbent articles.T. The method according to paragraphs R-S, wherein the digitized signalsare selected from the group consisting of: images; profile signals; 3-Dpoint cloud; height maps; and sensor data.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

Every document cited herein, including any cross referenced or relatedpatent or application and any patent application or patent to which thisapplication claims priority or benefit thereof, is hereby incorporatedherein by reference in its entirety unless expressly excluded orotherwise limited. The citation of any document is not an admission thatit is prior art with respect to any invention disclosed or claimedherein or that it alone, or in any combination with any other referenceor references, teaches, suggests or discloses any such invention.Further, to the extent that any meaning or definition of a term in thisdocument conflicts with any meaning or definition of the same term in adocument incorporated by reference, the meaning or definition assignedto that term in this document shall govern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. A method for inspecting absorbent articles, themethod comprising steps of: preprocessing each image of a group ofimages of absorbent articles to label a component of the absorbentarticle; generating a first inspection algorithm with a convolutionalneural network based on the preprocessed images, wherein the firstinspection algorithm is usable to mask the labeled component in imagesof absorbent articles; providing a second inspection algorithm, whereinthe second inspection algorithm is usable to determine one or moreproperties of the labeled component in images of absorbent articles,wherein the one or more properties comprise any of a location, a size, ashape, and a presence of the component; providing a sensor; providing acontroller, the controller comprising the first inspection algorithm;advancing a substrate through a converting process; creating images ofthe substrate with the sensor; communicating the images from the sensorto the controller; masking the labeled component in the images byanalyzing the images with the first inspection algorithm; determining atleast one of the properties for the labeled component with the secondinspection algorithm; cutting the substrate into discrete absorbentarticles; and based on the determination of the least one of theproperties for the labeled component, executing a control action,wherein the control action is any of rejecting one or more of thediscrete absorbent articles, an automatic phase adjustment, an automatictracking adjustment, a machine maintenance scheduling, and a machinestop command.
 2. The method of claim 1, wherein the controller comprisesany of a field programmable gate array, a graphics processing unit, anda central processing unit.
 3. The method of claim 1, wherein the labeledcomponent comprises any of a contaminant and an undesirabletransformation of the absorbent article.
 4. The method of claim 3,wherein the undesirable transformation of the absorbent articlecomprises any of a hole, a tear, and a wrinkle.
 5. The method of claim1, wherein determining the at least one of the properties for thelabeled component with the second inspection algorithm comprisesdetermining an angular orientation of the labeled component.
 6. Themethod of claim 1, wherein determining the at least one of theproperties for the labeled component with the second inspectionalgorithm comprises determining the absence of the labeled component. 7.The method of claim 1, wherein determining the at least one of theproperties for the labeled component with the second inspectionalgorithm comprises determining a texture of the labeled component. 8.The method of claim 1, wherein determining the at least one of theproperties for the labeled component with the second inspectionalgorithm comprises determining a total number of the labeledcomponents.
 9. The method of claim 1, wherein determining the at leastone of the properties for the labeled component with the secondinspection algorithm comprises determining a deformation of the labeledcomponent.
 10. The method of claim 1, wherein the absorbent articles areany of diapers and feminine hygiene products.
 11. The method of claim 1,further comprising a step of storing process data or product data.
 12. Amethod for inspecting absorbent articles, the method comprising stepsof: preprocessing each image of a group of images of absorbent articlesto label a component of the absorbent article; generating a firstinspection algorithm with a convolutional neural network based on thepreprocessed images, wherein the first inspection algorithm is usable tomask the labeled component in images of absorbent articles; providing asecond inspection algorithm, wherein the second inspection algorithm isusable to determine one or more properties of the labeled component inimages of absorbent articles, wherein the one or more propertiescomprise any of a location, a size, a shape, and a presence of thecomponent; advancing a substrate through a converting process; creatingimages of the substrate; masking the labeled component in the images byanalyzing the images with the first inspection algorithm; anddetermining at least one of the properties for the labeled componentwith the second inspection algorithm.
 13. The method of claim 12,further comprising: cutting the substrate into discrete absorbentarticles; and based on the determination of the least one of theproperties for the labeled component, executing a control action. 14.The method of claim 13, wherein the control action is any of rejectingone or more of the discrete absorbent articles, an automatic phaseadjustment, an automatic tracking adjustment, a machine maintenancescheduling, and a machine stop command.
 15. The method of claim 14,wherein determining the at least one of the properties for the labeledcomponent with the second inspection algorithm comprises any ofdetermining an angular orientation of the labeled component, determiningthe absence of the labeled component, determining a texture of thelabeled component, determining a total number of the labeled components,determining the presence of a contaminant, and determining anundesirable transformation of the labeled component.
 16. The method ofclaim 12, further comprising a step of storing process data or productdata.
 17. A method for inspecting absorbent articles, the methodcomprising steps of: storing a group of digitized signals of absorbentarticles, wherein the digitized signals are preprocessed to label acomponent of the absorbent article; providing the preprocessed digitizedsignals to a training algorithm to create a convolutional neuralnetwork; generating a first inspection algorithm with the convolutionalneural network based on the group of preprocessed digitized signals,wherein the first inspection algorithm masks one or more properties ofthe labeled component; providing a second inspection algorithm, whereinthe second inspection algorithm is usable to determine one or moreproperties of the labeled component in digitized signals of absorbentarticles, wherein the one or more properties comprise any of a location,a size, a shape, and a presence of the component; advancing a substratethrough a converting process; creating inspection data of the substratewith a sensor; communicating the inspection data from the sensor to acontroller, wherein the controller comprises the first inspectionalgorithm; identifying the labeled component by analyzing the inspectiondata with the first inspection algorithm; determining at least one ofthe properties for the labeled component with the second inspectionalgorithm; and cutting the substrate into discrete absorbent articles.18. The method of claim 17, further comprising the step of executing acontrol action based on the determination of the least one of theproperties for the labeled component, wherein the control action isselected from the group consisting of: adjusting an advancement speed ofthe substrate; adjusting a placement of component parts; and rejectingabsorbent articles.
 19. The method of claim 17, wherein the digitizedsignals are selected from the group consisting of: images; profilesignals; 3-D point cloud; height maps; and sensor data.
 20. The methodof claim 17, further comprising a step of storing process data orproduct data.